Method and device for display stream compression

ABSTRACT

A method and a device compress a display stream wherein coefficients are grouped, for each group, the greatest coded line index (GCLI) is determined and only the GCLI lowest weight bits of the coefficients are copied into the output stream together with the value of the GCLI. The method and device provide a good compression efficiency together with a simple hardware.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 61/771,165, filed Mar. 1, 2013, the entirety of which ishereby incorporated by reference into this application.

FIELD

The invention relates to a method for compressing an input data streamcomprising a sequence of m-bit words into an output data stream and fordecompressing said output data stream. The invention also relates to adevice for performing this compression method and a device forperforming said decompression method. The invention may be applicable toimage or video data streams.

DESCRIPTION OF PRIOR ART

The audio-visual market is rapidly evolving to ultra-high resolution(8192×4320 pixels) and higher frame rate. Real-time hardwarecompression/decompression systems therefore need to process data athigher and higher pixel rate. To address this problem, a known solutionis to either increase clock frequency of the processing circuit or toprocess several pixels in parallel during one clock cycle. As themaximum clock rate doesn't increase as fast as the demanded pixel rate,the only realistic solution is to process several pixels in parallel.Existing codecs usually achieve this by parallelizing several processingunits, each one working on different blocks of pixels. One should beaware that while parallelizing processing unit, the increase ofcomplexity comes not only from the increase of units, but also from theneed of a specific module that merges output of each unit and packs themcorrectly together. This results in an exponential increase ofcomplexity and power for each new technology generation.

Compression of a digital image is typically achieved in 3 steps:de-correlative transform, entropy coding and rate allocation.De-correlative transforms are applied to reduce the entropy of thetransformed image by concentrating probabilities of occurrence on asmall subset of coefficient values. De-correlative transforms commonlyused in image compression are colour transform, inter/intra prediction,DCT or wavelet transforms. The second step, entropy coding, make use ofthe results of the de-correlative transform to reduce the size of thetransformed image. Finally, rate allocation selects data that will bepart of the compressed image output stream to achieve the desiredcompression ratio.

Entropy coding codes a sequence of coefficients which are fixed-lengthbinary words into a sequence of variable length words. Numerous entropycoding methods exist, such as Fixed Length Coding, Variable LengthCoding, binary entropy coding (UVLC, zero-trees) or arithmetic codingwith various complexity and features.

Block Fixed Length Coding (BFLC) is usually done by block ofcoefficients. It consists in coding the coefficients with a reducednumber of bits which is determined by the maximum value of allcoefficients in the block. If the maximum value in a group of eightcoefficients is 5, each coefficient can be coded on 3 bits. Coding willthen consists in specifying the required number of bits and packing allnecessary bits of the coefficients (8*3 bits in the previous example) inthe output stream. This method exhibits a low complexity whileimplemented in software, but can require non negligible hardwarecomplexity when there is a need to process several coefficients inparallel, due to the output data packing process. Beside this, thecompression ratio reached is far below the theoretical ratio that couldbe reached with a perfect entropy coder coding the sequence ofcoefficients independently.

Variable Length Coding (VLC) is a little bit more complex but achievesbetter compression ratio. Each coefficient is coded using a variablelength binary code. The most probable values are coded with fewer bitsthan less probable values. It can be achieved using a table that storesthe variable length code for each possible input value. When theprobability of coding small values (around zero) is very high, the coderwill generate few bits, thus achieving a good compression ratio. A firstchallenge for this kind of compression scheme is to manage to stay closeof the optimum compression ratio predicted theoretically. Firstly,because reaching this optimum ratio requires adaptation of the variablelength codes to the probability distribution of coefficients value,which is not exactly known in practice. Secondly, because eachcoefficient must be coded with an integer number of bits, which resultsin a sub-optimal coding when probabilities are not an exact negativepower of two. Regarding hardware implementation, the second challenge ispacking together the variable length codes of several coefficients,which is more complex than in BFLC coding. Packing requires dynamicshifts, masking and “or” operations. While there is a need to work onmultiple pixels per clock cycle, complexity of the module in charge ofpacking and merging of each variable length code rises up dramatically.

Binary entropy coders such as UVLC [P. Delogne, B. Macq, Universalvariable length coding for an integrated approach to image coding,Annales des Télécommunications, Juillet/Aout 1991, Volume 46, Issue 7-8,pp 452-459] are processing coefficients bit per bit from the mostsignificant bitplane to the least significant bitplane. They are able toprocess multiple bits before producing an output bit and thus overcomethe problem of traditional Variable Length Coding regarding the loss incompression efficiency. UVLC (as well as zero-trees [A. Said, W. A.Pearlman, A new fast and efficient image codec based on set partitioningin hierarchical trees, IEEE Trans. Circuits Systems Video Technol. 6 (3)(June 1996) 243-250]) is splitting the coefficient's bit in two mainsubsets: significance bits and refinements bits. Significance bits areall bits from the MSB until the first ‘1’ (included), while refinementbits are all bits less significant than the first ‘1’ (those arerefining the precision of the decoded value). The probability of being‘0’ for a significance bit is usually high and it thus allows a goodcompression ratio, while refinement bits probability is around 0.5. Thegain of process entropy coding on the refinement bits is quite limitedbut requires as much complexity as processing it on significance bits.In the literature, several coders just skip the refinement bit codingand output it as is to reduce the coder complexity.

The most efficient entropy coders are based on binary arithmetic coding(CABAC in H.264 [D. Marpe, H. Schwarz, T. Wiegand, Context-BasedAdaptive Binary Arithmetic Coding in the H.264/AVC Video CompressionStandard, IEEE Trans. on circuits and systems for video technology, Vol.13, No. 7, July 2003] and EBCOT-MQ in JPEG2000 [D. Taubman, Highperformance scalable image compression with EBCOT, IEEE Trans. ImageProcess. 9 (7) (July 2000) 1158-1170.]). Each bit of the coefficients isassociated with its probability of being ‘0’ or ‘1’. This probabilitycan be estimated in numerous ways, from really simple to extremelycomplex ones. The probability is used to subdivide one interval intoseveral smaller ones, and the coded bit selects which interval is keptto encode the next bit (Elias coding). This coding scheme allowsreaching a rate very close to the entropy level of the coded sequence ofbits. However, encoding a single bit requires several arithmeticoperations, making it very resource consuming.

A method for entropically transcoding a first binary data stream into asecond compressed data stream is known from WO2010026351. Referring topage 10 and FIG. 1 of this document, the method comprises a statisticalanalysis of a first sequence of data in step 102 to determine the valueof B as integer part of the average of the positions of thehighest-order bit at “1” of the words of the data set. Afterwards, theactual encoding of all words is performed in steps 104 to 110. Onlyafter these operations, the output sequence of data is prepared andsent. It implies a high latency for such a method. In addition, in thismethod, each word of the data set is treated sequentially (steps 104 to110). Therefore this method implies either a high hardware complexityfor obtaining a given throughput or a lower throughput with hardware oflimited complexity.

Document US20100232497 discloses a lossless and near-lossless imagecompression method and system. More specifically, at FIG. 5 and atparagraphs 67-74 of said document, an encoding scheme is described wheresuccessive sample sizes are adapted according to predicted sample sizeswhich are obtained from the sizes of previously coded samples. Thepredicted size may be obtained by computing the average of the sizes ofthe samples of previous components

Many encoding methods are known, which attempt to achieve a bettercompression. However, these methods imply an increased computational andstorage requirement, which make them inapplicable to the highresolutions and high frame rates.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a method and devicefor compressing an input data stream into an output data stream and fordecompressing said output data stream having an acceptable compressionefficiency while minimising codec complexity, especially in a context oflow compression ratio (such as 2:1 to 4:1) for high throughputapplications.

The invention is defined by the independent claims. The dependent claimsdefine advantageous embodiments.

According to a first aspect of the invention, there is provided a methodfor compressing an input data stream comprising a sequence of words of mbits into an output data stream, comprising the steps of: a)groupingsaid words of said sequence into one or more groups of n words of mbits, n being greater than or equal to 2; b)detecting for each group thevalue of the Greatest Coded Line Index (GCLI), the GCLI being the indexof the highest weight non-zero bit among the bits, excluding any signbit, of the words in said group; c) producing an output data comprisingone or more groups of n words of GCLI bits corresponding respectively tothe n words of m bits in a corresponding group in the input stream,where the GCLI bits of each word in the output stream are the GCLI bitsof lowest weight of the corresponding word in the input stream, and thevalue of the GCLI; d) producing an output data stream comprising saidoutput data. The index of the lowest significant bit in a word iscounted as 1, and indexes are increasing by 1 for each successive higherweight bits.

Preferably, a de-correlative transform step is performed on the inputdata stream prior to said grouping step.

Said de-correlative transform may advantageously be a DWT 5/3 wavelettransform based on a filter bank.

The value of n may be selected smaller than or equal to 8 or morepreferably n is equal to 4.

When said words of m bits are represented as sign-magnitude and comprisea sign bit, said sign bit is copied to the output data together with thecorresponding word of GCLI bits. Optionally, said sign bit is not copiedto the output data when corresponding word of GCLI bits is zero.

The GCLI's may advantageously be replaced by entropic coding thereof,more advantageously an unary coding.

According to a preferred embodiment, a sequence of groups of n words ofm bits correspond respectively to a sequence of n pixels in a row of adisplay image comprising rows and columns of pixels. The method then maycomprise between above steps (c) and (d) the steps of

replacing the GCLI's of the second to last groups corresponding to thefirst row by the difference between the GCLI of said group and anaverage of the GCLI's of one or more of the previous groups in saidsequence ;

replacing the GCLI's of the groups in the subsequent rows by thedifference between the GCLI of said group and the GCLI of thecorresponding group in the previous row and in the same column. In thisembodiment, it is necessary to buffer only the GCLI's of the groups of arow, and not the coefficients of the pixels of a row.

According to another preferred embodiment, said group of n words of mbits being are considered in successive bit planes of decreasingweights. The above step d) may then comprise copying the successive bitplanes, starting with the highest-order bit plane up to thelowest-significant bit plane in the output data stream. In thisembodiment, it is easy to reduce the volume of data or the requiredbandwidth, if necessary, by simply cutting some of the lowest weightbitplanes of the output data.

According to a second aspect of the invention, there is provided amethod for decompressing an input data stream comprising a sequence ofgroups of n words of GCLI bits, and for each group the value of GCLI,obtainable by the method of the invention, into an output data stream,comprising the step of producing an output data stream comprising foreach word of GCLI bits of each group of the input stream, a word of mbits equal to the GCLI lowest weight bits of said words of m bits, andbits at zero for the (m-GCLI) highest-weight bit words.

According to a third aspect of the invention, there is provided a devicefor compressing an input data stream comprising a sequence of words of mbits into an output data stream, comprising:

means for grouping said words of said sequence into one or more groupsof n words of m bits, n being greater than or equal to 2;

means for detecting for each group the value of the Greatest Coded LineIndex (GCLI), the GCLI being the index of the highest weight non-zerobit among the bits, excluding any sign bit, of the words in said group;

means for producing an output data comprising one or more groups of nwords of GCLI bits corresponding respectively to the n words of m bitsin a corresponding group in the input stream, where the GCLI bits ofeach word in the output stream are the GCLI bits of lowest weight of thecorresponding word in the input stream, and the value of the GCLI;

means for producing an output data stream comprising said output data.

Said means for grouping may comprise a set of n registers of m bits forstoring n words of m bits from the input stream.

Said means for detecting the GCLI may comprise m logical OR-gates havingas input the n bits of a bit plane.

According to a fourth aspect of the invention, there is provided adevice for decompressing an input data stream comprising a sequence ofgroups of n words of GCLI bits, and for each group the value of GCLI,obtainable by the method of any of claims 1 to 8, into an output datastream, comprising: means for producing an output data stream comprisingfor each word of GCLI bits of each group of the input stream, a word ofm bits equal to the GCLI lowest weight bits of said words of m bits, andzero bits for the (m-GCLI) highest-weight bit words.

SHORT DESCRIPTION OF THE DRAWINGS

These and further aspects of the invention will be explained in greaterdetail by way of example and with reference to the accompanying drawingsin which:

FIG. 1 shows schematically the data as used in the method forcompressing of the invention;

FIG. 2 is a flow-chart representing the operations performed in themethod of the invention;

FIG. 3 is a bloc diagram of the hardware of a device according to theinvention;

FIG. 4 represents the output rate in bits per pixel (bpp) in dependenceof the group size n, for a known method and the method of the invention;

FIG. 5 is a schematic representation of a possible implementation of adevice according to the invention;

The drawings of the figures are neither drawn to scale nor proportioned.Generally, identical components are denoted by the same referencenumerals in the figures.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

FIG. 1 shows schematically an example of the data as used in the methodfor compressing of the invention. A group of four words (n=4) of m bitsof the input stream are represented at the left of FIG. 1, with thelowest significant bits (LSB) at the bottom and the most significantbits (MSB) at the top. A bitplane is the set of bits in the group havingsame weight. The line index of a bitplane is 1 for the LSB biptlane, andincreases upwards. In this example, the words of m bits are representedas sign and magnitude. It is understood that the other knownrepresentation of binary numbers (Offset binary, 2's complement, 1'scomplement) may be used also, and that the method of the invention isapplicable to these representations, e.g. by first transforming thenumbers to the sign and magnitude representation. In the example of FIG.1, the GCLI is 4: all bitplanes above the fourth contain only zeros(except for the optional sign bit plane), while the fourth bitplanecontains at least one ‘1’. According to the method of the invention, thesign bits as well as the GCLI bits of lowest weight, i.e. the dataencircled in the right hand side of FIG. 1, are copied to the outputstream, in a raw transfer. The words of m bits are also known ascoefficients, in the field of image processing. For each group, aGreatest Coded Line Index, GCLI, is found. The GCLI is the line index ofthe most significant non-null bitplane. The GCLI may then be entropycoded and sent to the output stream. The entropy coding method used forcoding the GCLI sequence is preferably an easy-to-implement Unary Codingmethod. If there is at least one non-null bitplane in the group(GCLI>0), the GCLI bitplane, as well as all less significant bitplanesand the sign bitplane are packed in a RAW mode in the output stream.FIG. 1 shows how GCLI can be found from a bitplane representation of thecoefficients.

While Fixed Length Coding of the GCLIs already offers an interestingcompression ratio, improvement has been brought on top of it to furtherincrease compression ratio while still keeping the low complexity of thesolution. It consists in reducing bandwidth needed to transmit GCLIvalues in the output stream. This solution is detailed hereafter. Theinput data stream corresponds to a display image having rows and columnsof pixels. GCLIs are processed in two steps. In a first step ofhorizontal prediction, on the first row of each image, GCLIs arepredicted as a horizontal combination of its previous neighboursbelonging to the same row and the same wavelet subband. The symbol codedis the difference between the GCLI value and the predicted value of theGCLI. In a second step, a vertical prediction is performed between tworows of GCLIs. The result is the difference between the GCLI value andthe corresponding GCLI of the same subset of coefficients in thepreviously coded row. Predictive values may afterwards be codedfollowing an easy-to-implement Unary Coding method (Table 1).

TABLE 1 A simple unary code for GCLI coding. Data Unary code . . . . . .−2 0011 −1 011 0 0 1 010 2 0010 . . . . . .

FIG. 2 is a flow-chart representing the operations performed in themethod of the invention. Successive coefficients are acquired andbuffered. When coefficients of the input stream have been buffered, thecoefficients are grouped in groups of n, preferably four. Coefficientsof a group belong to the same component, row and subband of the inputstream. The GCLI of each group is determined as discussed above. If theGCLI of a group is not equal to zero, the raw data, i.e. the GCLI lowestweight bits of the coefficients of the group are copied to an outputbuffer and the sign bit plane. The GCLIs are coded and stored in theoutput buffer with corresponding group.

FIG. 3 is a bloc diagram of the hardware of a device according to theinvention for performing a new entropic coding scheme. This new entropycoder is used in the context of a digital image compression algorithm.Starting from the top at FIG. 3, a Reversible Colour Transform (RCT) mayoptionally be performed, allowing a first de-correlation step betweenthe 3 colour components of the image. A second optional step ofde-correlative transform may then be applied on the input picture priorto the entropy coding. Many known de-correlative transforms may be used.As an example embodiment, the de-correlative transform that has beenused for the assessment of this new solution may be a wavelet transformbased on a filter-bank first presented in [D. Le Gall, A. Tabatabai,Subband coding of digital images using symmetric kernel filters andarithmetic coding techniques, in: Proceedings of the InternationalConference on Acoustics, Speech Signal Processing, N.Y., USA, April1988, pp. 761-764.], and commonly referred as the DWT 5/3. In order tominimize complexity, this filter was only applied horizontally (line byline), and no vertical de-correlative transform was applied. Thishorizontal DWT 5/3 can be applied several times recursively, the numberof iterations in our algorithm varying from 1 to 8. Coefficients at theoutput of the DWT 5/3 transform are then entropy coded using the newproposed scheme. The entropy coding (dashed block on FIG. 3) comprisesthe hardware for performing GCLI extraction, optional GCLI predictionand GCLI coding, as discussed above. The raw data buffer transfers thepart of the input stream, i.e. the sign bitplane and the GCLIlower-weight bitplanes of the input stream. On the rate allocation side,the chosen solution is also extremely simple and easy to implement. Itcomprises trimming the less significant bitplanes of an output packet,in order to reach the targeted output rate. This solution allowsrealisation of rate allocation without any recoding iteration of thedata, but only in managing to keep the good amount of data. In order toreach an optimal quality for the decoded output image, the rateallocation is done on several groups of coefficient, and by weightingthe amount of data that must be trimmed for each group. Variousmechanisms for determining the amount of data that must be trimmed foreach group are known and can be used in the invention.

FIG. 4 represents the output rate in bits per pixel (bpp) in dependenceof the group size n, for a known method and the method of the invention.The input stream of this simulation comprises three eight bit words perpixels (e.g. RGB coefficients, or YUV coefficients), i.e. 24 bpp (bitsper pixel). The two curves represent the number of bits per pixel aftercompression. The dashed curve represents the results obtained with BFLC(Block Fixed Length Coding) and the continuous curve represents theresults obtained with the method of the invention, for different valuesof n, the group size. It can be seen that the method according to theinvention requires much less bits per pixels, i.e. gives a bettercompression. One obtains a better compression with a group size of 1(i.e. no grouping), however if no grouping is performed, high date ratescan only be obtained with parallelizing, therefore at an increased costand complexity. Output rate of a standard BFLC method is directly linkedto the number of coefficients in each block. There is a trade-offbetween the bandwidth used by the GCLI information (which rises up ifthe blocks are smaller), and the bandwidth used by raw data (which risesup while the blocks are bigger). Considering a 4 bits code for eachGCLI, FIG. 4 shows the output rate reached (in bits per pixel) withregards to the size of the block for one test picture de-correlatedusing DWT 5/3 horizontal transformation. Note that when block size is 1,the most significant non-null bit is not coded since it is always ‘1’.One can see that in these conditions, the best compression ratio isreached while using block size of 8 coefficients and output rate isaround 15 bpp. Entropy coding the GCLI values according to the inventiondrastically changes the trade-off since GCLI bandwidth is modified. FIG.4 shows the output rate that can be obtained while using a perfectentropy coder for coding the GCLI sequence (size of RAW data+[entropy ofthe GCLI sequence]×[size of GCLI sequence]). The best compression ratiois achieved when using block size of 1, thus working coefficient percoefficient. In fact, this corner case is perfectly equivalent to abinary entropy coding method that uses the significance/refinementsplit. It achieves theoretically the same output rate (when refinementbits coding is skipped) but requires also the same high hardwarecomplexity. FIG. 4 also shows that using block of four coefficientsrepresents a limited increase in output rate (13 bpp to 13.5 bpp i.e.around 4%) in comparison to a binary entropy coder which use thesignificance/refinement split (group size of 1 in the graph—sig/refbinary coding). On the other hand, this small increase of the outputrate is compensated by the really low hardware complexity needed toimplement this solution. The hardware complexity can be estimated as thesum of a BFLC coder (which is really low), and the complexity of themodule used to code the

GCLI sequence. As the needed throughput for this second module is verylow (it needs to process 1 GCLI of 4 bits instead of 4 coefficients of16 bits), the complexity of this module remains far below (at least 16times) the complexity of the binary entropy coder that should encode the4 coefficients of 16 bits. Furthermore, as this coder works on severalcoefficients at a time, it intrinsically allows processing severalpixels at a time. BFLC combined with entropy coding of GCLI values seemsthus to present a great trade-off between hardware complexity andcompression efficiency.

FIG. 5 is a schematic representation of a possible architecture for ahardware implementation of a device for compressing an input datastream. The number n of groups of words in this example is 4. The meansfor grouping may comprise 4-input or-gates and a set of 4 registers forstoring the 4 words of a group. The means for detecting the GCLI (“GCLIfinding”) is implemented with a set of logic gates. It emphasizes theextremely low complexity of the solution, especially if it is taken intoaccount that it allows to encode 4 coefficients on the same clock cycle.In this figure, GCLI finding, GCLI prediction and unary codingarchitectures are not fully specified in a concern of simplicity. Inthis example, a fast bitwise OR is performed on the four inputcoefficients of a group. The GCLI is computed as the first ‘1’ met,starting from the MSB, sign bit excepted. It is output an 4 bits. Onecan easily be convinced on the small hardware complexity of theseelements : this block exhibits a very low logical complexity, as it onlyconsists of some logical gates. It mainly comes from the fact that thethroughput of data that must be processed is not excessive. Regardingthe case of an 8K image @60Hz, this block needs to process 1.5 G GCLI/s.Considering a 4 bits weight for each GCLI, the GCLI coding needs thus toprocess 6 Gb/s, which is far below the input data rate of 80 Gb/s neededfor an 8K image @60Hz (3 components/12-bit per component).

This block also needs a buffer to store all GCLI of the previous line,needed to achieve vertical prediction of the GCLI. Its size can beroughly estimated as 24 Kbit for the worst case of 8K image resolution.The size of this buffer scales proportionally with the resolution widthof the image. The means for producing the output data and the means forproducing the output data stream are implemented using a set ofregisters and gates. A corresponding device for decompressing an inputdata stream can be implemented using similar and corresponding hardware.These hardware may be implemented, as well known in the art, usingindividual gates and registers, ASICs or FPGAs.

Advantages brought by the compression method of the invention are:

Processing is much simpler than in other compression scheme.

Compression efficiency reduction represents a nice trade-off withregards to complexity.

Packing the output codestream is simplified.

Rate allocation process is simple and requires no feedback loops ormulti-pass encoding (like PCRD optimizations in JPEG2000).

The method of the invention achieves compression of a group ofcoefficients in a few steps, in an extremely simple and effective wayfor hardware implementation. As this compression scheme encodes severalpixels at the same time, parallel encoding of multiple pixels isintrinsic to the proposed codec. It allows reaching high pixel rate witha low complexity codec, while keeping good compression efficiency.

The present invention has been described in terms of specificembodiments, which are illustrative of the invention and not to beconstrued as limiting. More generally, it will be appreciated by personsskilled in the art that the present invention is not limited by what hasbeen particularly shown and/or described hereinabove.

Reference numerals in the claims do not limit their protective scope.Use of the verbs “to comprise”, “to include”, “to be composed of”, orany other variant, as well as their respective conjugations, does notexclude the presence of elements other than those stated. Use of thearticle “a”, “an” or “the” preceding an element does not exclude thepresence of a plurality of such elements.

The invention may also be described as follows: the invention provides amethod and device for compressing a display stream wherein coefficientsare grouped, for each group, the greatest coded line index (GCLI) isdetermined and only the GCLI lowest weight bits of the coefficients arecopied into the output stream together with the value of the GCLI. Theinvention provides good compression efficiency together with a simplehardware.

1. A method for compressing an input data stream comprising a sequenceof words of m bits into an output data stream, the method comprising thesteps of: a) grouping said words of said sequence into one or moregroups of n words of m bits, n being greater than or equal to 2; b)detecting for each group the value of the Greatest Coded Line Index(GCLI), the GCLI being the index of the highest weight non-zero bitamong the bits, excluding any sign bit, of the words in said group; c)producing an output data comprising one or more groups of n words ofGCLI bits corresponding respectively to the n words of m bits in acorresponding group in the input stream, where the GCLI bits of eachword in the output stream are the GCLI bits of lowest weight of thecorresponding word in the input stream, and the value of the GCLI; d)producing an output data stream comprising said output data.
 2. Themethod according to claim 1, wherein a de-correlative transform step isperformed on the input data stream prior to said grouping step.
 3. Themethod according to claim 2, wherein said de-correlative transform is aDWT 5/3 wavelet transform based on a filter bank.
 4. The methodaccording to claim 2, wherein said sequence of words of m bitscorresponds to a sequence of pixels in a row of a display imagecomprising rows and columns of pixels and that said de-correlativetransform is performed on said sequence of pixels in a row.
 5. Themethod according to claim 1, wherein n is smaller than or equal to
 8. 6.The method according to claim 5, wherein n is equal to
 4. 7. The methodaccording to claim 1 wherein said words of m bits are represented assign-magnitude and comprise a sign bit, and that said sign bit is copiedto the output data together with the corresponding word of GCLI bits. 8.The method according to claim 1 comprising between said step (c) andsaid step (d) the step of replacing the GCLI's by an entropic codingthereof.
 9. The method according to any of claim 8 wherein said entropiccoding is a unary coding.
 10. The method according to claim 1, wherein asequence of groups of n words of m bits corresponds respectively to asequence of n pixels in a row of a display image comprising rows andcolumns of pixels, and comprising between said step (c) and said step(d) the steps of replacing the GCLI's of the second to last groupscorresponding to the first row by the difference between the GCLI ofsaid group and an average of the GCLI's of one or more of the previousgroups in said sequence; replacing the GCLI's of the groups in thesubsequent rows by the difference between the GCLI of said group and theGCLI of the corresponding group in the previous row and in the samecolumn.
 11. The method according to claim 1, said group of n words of mbits being considered in successive bit planes of decreasing weights,wherein the step of producing an output data stream comprises copyingthe successive bit planes, starting with the highest-order bit plane upto the lowest-significant bit plane in the output data stream.
 12. Themethod for decompressing an input data stream comprising a sequence ofgroups of n words of GCLI bits, and for each group the value of GCLI,obtainable by the method of claim 1, into an output data stream,comprising the step of: producing an output data stream comprising foreach word of GCLI bits of each group of the input stream, a word of mbits equal to the word of GCLI bits for the GCLI lowest-weight bits ofsaid word of m bits, and zero bits for the (m-GCLI) highest-weight bitwords.
 13. A device for compressing an input data stream comprising asequence of words of m bits into an output data stream, the devicecomprising: means for grouping said words of said sequence into one ormore groups of n words of m bits, n being greater than or equal to 2;means for detecting for each group the value of the Greatest Coded LineIndex (GCLI), the GCLI being the index of the highest weight non-zerobit among the bits, excluding any sign bit, of the words in said group;means for producing an output data comprising one or more groups of nwords of GCLI bits corresponding respectively to the n words of m bitsin a corresponding group in the input stream, where the GCLI bits ofeach word in the output stream are the GCLI bits of lowest weight of thecorresponding word in the input stream, and the value of the GCLI; meansfor producing an output data stream comprising said output data.
 14. Thedevice according to claim 13, wherein said means for grouping comprise aset of n registers of m bits for storing n words of m bits from theinput stream.
 15. The device according to claim 13, wherein said meansfor detecting the GCLI comprises m logical OR-gates having as input then bits of a bit plane.
 16. A device for decompressing an input datastream comprising a sequence of groups of n words of GCLI bits, and foreach group the value of GCLI, obtainable by the method of claim 1, intoan output data stream, comprising: means for producing an output datastream comprising for each word of GCLI bits of each group of the inputstream, a word of m bits equal to the word of GCLI bits for the GCLIlowest-weight bits of said word of m bits, and zero bits for the(m-GCLI) highest-weight bit words.